AttributeError: 'Tensor' object has no attribute 'numpy'
PythonNumpyTensorflowAttributeerrorTensorPython Problem Overview
How can I fix this error I downloaded this code from GitHub.
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()
throws the error
AttributeError: 'Tensor' object has no attribute 'numpy'
Please help me fix this!
I used:
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
And i get this error. Someone help me i just want it to work why is this so hard?
D:\Python>python TextGenOut.py
File "TextGenOut.py", line 72
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
^
IndentationError: unexpected indent
D:\Python>python TextGenOut.py
2018-09-16 21:50:57.008663: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-09-16 21:50:57.272973: W T:\src\github\tensorflow\tensorflow\core\framework\op_kernel.cc:1275] OP_REQUIRES failed at resource_variable_ops.cc:480 : Not found: Container localhost does not exist. (Could not find resource: localhost/model/embedding/embeddings)
Traceback (most recent call last):
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1278, in _do_call
return fn(*args)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1263, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1350, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
[[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "TextGenOut.py", line 72, in <module>
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 680, in eval
return _eval_using_default_session(self, feed_dict, self.graph, session)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 4951, in _eval_using_default_session
return session.run(tensors, feed_dict)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 877, in run
run_metadata_ptr)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1100, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1272, in _do_run
run_metadata)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\client\session.py", line 1291, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.FailedPreconditionError: Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
[[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]
Caused by op 'model/dense/MatMul/ReadVariableOp', defined at:
File "TextGenOut.py", line 66, in <module>
predictions, hidden = model(input_eval, hidden)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\base_layer.py", line 736, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "TextGenOut.py", line 39, in call
x = self.fc(output)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\engine\base_layer.py", line 736, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\keras\layers\core.py", line 943, in call
outputs = gen_math_ops.mat_mul(inputs, self.kernel)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\gen_math_ops.py", line 4750, in mat_mul
name=name)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\op_def_library.py", line 510, in _apply_op_helper
preferred_dtype=default_dtype)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 1094, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1045, in _dense_var_to_tensor
return var._dense_var_to_tensor(dtype=dtype, name=name, as_ref=as_ref) # pylint: disable=protected-access
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 1000, in _dense_var_to_tensor
return self.value()
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 662, in value
return self._read_variable_op()
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\resource_variable_ops.py", line 745, in _read_variable_op
self._dtype)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\ops\gen_resource_variable_ops.py", line 562, in read_variable_op
"ReadVariableOp", resource=resource, dtype=dtype, name=name)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\util\deprecation.py", line 454, in new_func
return func(*args, **kwargs)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 3155, in create_op
op_def=op_def)
File "C:\Users\fried\AppData\Roaming\Python\Python36\site-packages\tensorflow\python\framework\ops.py", line 1717, in __init__
self._traceback = tf_stack.extract_stack()
FailedPreconditionError (see above for traceback): Error while reading resource variable model/dense/kernel from Container: localhost. This could mean that the variable was uninitialized. Not found: Container localhost does not exist. (Could not find resource: localhost/model/dense/kernel)
[[Node: model/dense/MatMul/ReadVariableOp = ReadVariableOp[dtype=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](model/dense/kernel)]]
Python Solutions
Solution 1 - Python
I suspect the place where you copied the code from had eager execution enabled, i.e. had invoked tf.enable_eager_execution()
at the start of the program.
You could do the same.
UPDATE: Note that eager execution is enabled by default in TensorFlow 2.0. So the answer above applies only to TensorFlow 1.x
Solution 2 - Python
Since the accepted answer did not solve the problem for me so I thought it might be helpful for some people who face the problem and that already have tensorflow version >= 2.2.0 and eager execution enabled.
The issue seems to be that for certain functions during the fitting model.fit()
the @tf.function
decorator prohibits the execution of functions like tensor.numpy()
for performance reasons.
The solution for me was to pass the flag run_eagerly=True
to the model.compile()
like this:
model.compile(..., run_eagerly=True)
Solution 3 - Python
Tensorflow 2 has a config option to run functions "eagerly" which will enable getting Tensor values via .numpy()
method. To enable eager execution, use following command:
tf.config.run_functions_eagerly(True)
Note that this is useful mainly for debugging.
See also: https://www.tensorflow.org/api_docs/python/tf/config/run_functions_eagerly
Solution 4 - Python
This can also happen in TF2.0 if your code is wrapped in a @tf.function or inside a Keras layer. Both of those run in graph mode. There's a lot of secretly broken code out of there because behavior differs between eager and graph modes and people are not aware that they're switching contexts, so be careful!
Solution 5 - Python
It happens in older version of TF. So try pip install tensorflow --upgrade
otherwise run
import tensorflow as tf
tf.enable_eager_execution()
If you are using Jupyter notebook, restart the Kernel.
Solution 6 - Python
tf.multinomial
returns a Tensor object that contains a 2D list with drawn samples of shape [batch_size, num_samples]
. Calling .eval()
on that tensor object is expected to return a numpy ndarray.
Something like this:
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
You also need to ensure that you have a session active (doesn't make a lot of sense otherwise):
sess = tf.Session()
with sess.as_default():
predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].eval()
Solution 7 - Python
For people who still have this problem in TF 2.0.0 run: tf.config.run_functions_eagerly(True) top of ur program it works perfectly!
Solution 8 - Python
I saw similar error when I run code something like the following,
tensor = tf.multiply(ndarray, 42)
tensor.numpy() # throw AttributeError: 'Tensor' object has no attribute 'numpy'
I use anaconda 3 with tensorflow 1.14.0. I upgraded tensorflow with the command below
conda update tensorflow
now tensorflow is 2.0.0, issue fixed. Try this to see if it resolves your issue.
Solution 9 - Python
I had the same issue in a tf.function(): But what has worked for me is to transform the numpy array into a tensorflow tensor via tf.convert_to_tensor
Doku and then go ahead with tensorflow. Maybe this trick could be useful for anyone...